An Introduction to Customer Data Analysis
We define customer data analysis, before sharing how it can be used as a tool to improve performance across the whole organisation.
What Is Customer Data Analysis?
Customer data analysis is the process of looking for patterns in your customer data, with the intention of improving a certain part of your organisation’s performance.
This last part is key, according to Duncan White, the Managing Director of horizon2.
“Analysis by itself doesn’t deliver any improvement. Just because you can see an organisational improvement opportunity, that doesn’t mean that it is being delivered,” says Duncan.
So, as we go forwards with our customer data analysis, we should remember these three golden rules, as recommended by Duncan:
- No measurement without recording
- No recording if you are not going to analyse it
- No analysis without action
This last point is particularly key, because if we are going to analyse anything, you’ve got to have a performance initiative on the back of it.
Remember: “You don’t fatten a pig just by weighing it.”
What Are the Benefits of Customer Data Analysis?
Making improvements based of good customer data analysis has a whole range of benefits.
Of course it depends very much on what you analyse, but five classic benefits will include:
- Segmenting your customer base – Through customer data analysis, we can split our customer base by age, location, satisfaction etc., so we can send more relevant promotional offers and best adapt our service approach.
- Detecting root cause problems – Data may tell us common issues in how customers are using our product and contact us. It also allows us to perform “narrative analysis”, so we understand why customers feel the way they do about us.
- Understanding customer journeys – Analysing customer data will tell us how customers are feeling at certain points in their experience. It therefore shows us where exactly we most need to improve.
- Identifying customer needs – Using our data, we track which point in the customer journey they have reached, so that when they phone through to the contact centre, we present all information to the advisor, who can better identify the customers needs.
- Predicting customer needs – By analysing customer behaviour, we can predict their next steps. From this analysis, we can then add steps to the customer journey to simplify their experience. Perhaps an proactive message?
Which Types of Customer Data do We Have Access to?
In our research we have found three key subsets of customer data, which can be analysed for different purposes. These are:
1. Personal Data
Personal data is often the data that you collect when the customer first starts doing business with you. This includes things like:
- The customer’s full name
- Home address
- Contact details
- Date of birth
- Credit/debit card details
We can also collect data like this by using device IDs, while we can gather more personal information through the use of website cookies and IP addresses.
The benefit of capturing this information is simple; it allows us to recognise individual customers and therefore offer a personalised service.
This data can be used to create customer personas and also to see how engagement varies by things like age and location.
2. Engagement Data
Engagement data tells you how customers are interacting with your brand and helps you to uncover different patterns in how customers interact with you, right across their journey.
So, by collecting engagement data, we are gathering insights, such as:
- Transactional data
- Channel preference
- Churn rates
- Social media engagement
- Advert engagement (response to promotions)
- Product/service usage
We can also do things like use heat maps on our website to identify how customers are engaging with us online and where they are struggling – if we have an online improvement initiative to back it up.
By collecting engagement data like this, we will have so many things to analyse, in terms of customer behaviour.
For example, we can track how customers react to new promotion, a new contact centre channel or any other changes within the customer journey. We can also see how they are using our products.
3. Sentiment Data
Sentiment data gives you insights into the feelings and emotions of your customers. This data is usually gathered through contact centre surveys and analytics.
With this in mind, when we collect sentiment data, we are looking at things like:
- Customer Satisfaction
- Net Promoter Score
- Customer Emotion
- Product desirability
- Customer preferences
Sentiment data helps us to add context to engagement data. For example, pairing advert engagement data and customer satisfaction will tell you who you should send a certain marketing campaign to.
Also, by collecting insights into how customers are feeling, simple analysis will tell us who is likely to churn and therefore save. This can have a big impact on revenues.
Another benefit is that sentiment data will really help to show you the points in your customer journey that help you to stand out from the crowd.